PAWS

1. Application Requirements • System Specifications • Competitors • Block Diagram • Timeline • MDR Specifications PAWS Precise Autonomous Wildlife Surveillance • Outline • Motivation • Requirements • Design • Prototype • Outlook

2. Contemporary Societal Context • Environmental Concerns • Sprawling human infrastructure and influence reducing wildlife habitats worldwide • Wildlife at risk of extinction • Political Context • Government protection under Endangered Species Act needed to preserve rare species • Considerable data is needed to support a claim for government protection

3. was filed based on new evidence Examples California Wolverine • Thought to be extinct • Recently rediscovered by wildlife camera • Application for protection under Endangered Species Act Asiatic Cheetahs • Fewer than 60 exist on the entire Asian continent • Hunted to near extinction • Efforts being made to protect natural populations Cambodian Tiger • Rare tiger photographed on Sumatra • Photos used to estimate population density and establish range maps “Camera Traps.” worldwildlife.org. 8 October 2008. <http://www.worldwildlife.org/species/camera-traps/cameratraps.html>

4. Examples Asian Elephants • Rare species found deep in Sumatran Forest • Area of forest where pictures taken deemed • “worthless” by local government • Forest earmarked for oil palm and timber plantations Clouded Leopard Sumatran Tiger http://www.newscientist.com/article/dn12852-camera-traps-reveal-secrets-of-worthless-forest.html

5. Potential Consumers • Conservationist Organizations seeking government protection for rare species • Monitor movements and migratory patterns • Evidence and data to support or discredit claims for endangered species classification • Wildlife Documentaries • Rare footage • Motion tracking will allow for higher quality video • Wildlife Scientists • Involved in various studies necessitating wildlife surveillance

6. Application Requirements (details in subsequent slides) • Noise • Sensitivities of wildlife must be considered • Pan Speed • Trade-off between capturing at average speed or slower speed • Resolution / Frame Rate • Ability to identify distinguishable features in targeted wildlife • Maximizing data throughput and minimizing tracking delay • Environmental Conditions • Outdoor application • Cost • Main competitor-camera traps ~$700

7. Applying Realistic Constraints • Noise • Tested for noise levels and evaluated to be within a, yet to be determined, permissible audible range. • Environment • Components can withstand 5 - 45 ºC and Humidity range 20 - 80% RH (non-condensing) • Optional system case will extend operability ranges • Cost • Prototype is within cost specification (excluding computer) • Cost will be variable on account of the range of cameras and quality available

8. Applying Realistic Constraints- Experimentation • Vision Range • 360 Degrees Lateral Pan • Angle of View from Camera • 48○horizontal • 30○vertical • At 15 feet allows you to see 12 feet up a tree • Pan Speed • Given a target moving 20 mph at 15 feet, • Angular Velocity 111 Degrees/sec 3.24s / revolution • Servo chosen to meet application requirement • RECALL: NOT tracking animals at top speeds • Power • Currently a computer is being used for computing and the source of power- FPGA in the future? • 2 week sustainability is the goal

9. Flow Diagram Deployment of PAWS (Wait for Motion) Motion Vector Analysis No Storage Images Sensor Detection? Recording Video Target Relocated? No Target Located End Video Return to Dormant State data No Time Out Yes PWM MCU Position Adjustment Algorithm Servo with Camera Yes

10. Block Diagram Ethernet Camera High Powered Microprocessor Image Processor Memory MCU Servo Serial PWM Motion Sensor Array

11. Prototype • Original MDR Specs • Camera, Servo, MCU, & Motion Sensors Interfaced • Identify quadrants w/ motion sensors and move servo accordingly • Successfully track an simple object against a simple background • MDR Progress • 3 major interfaces completed • Established basis for extracting pixels and identifying the center of a target object

12. Motion Sensor Array to MCU Signal out of Sensor Discharging Capacitor Charging Capacitor High and Low Strobe while detecting motion Capacitor discharging when motion is detected. Complete discharge will initiate system. Capacitor is charging Time Constant is adjustable using a potentiometer for the resistor in the RC Circuit. Adjusting τadjusts the sensitivity level of the system.

13. MCU to Servo • Each duty cycle corresponds to a discrete location • Using Servo library in MCU, Pulse Width Modulation generated Duty Cycle Duty Cycle - .9 ms 0 ° Period 90 ° Duty Cycle – 1.2 ms 180 ° Duty Cycle – 1.5 ms Duty Cycle – 1.8 ms 270 °

14. MCU to Servo Frames Isolation of a Target Center of Target

15. Prototype • Unexpected obstacles • Complexity of real time image processing • Streaming video into program • Extracting frames intermittently • Establishing serial connection from PC to MCU • Various serial connection libraries difficult to implement due to complexity of Operating Systems

16. Responsibility Distribution / Progression • Jason Dvorsky • Part research, selection, and purchase • Interfacing servomotor and motion sensors • Timothy Somerville • Part research, selection, and purchase • Camera and obtaining image data • Kelvin Chan • Interfacing servomotor and motion sensors • Camera and obtaining image data • Taylor Caggiano • Report and presentation expectations • Motion analysis options for the application

17. Outlook • CDR Goals • Extract frames from streaming video • Identify object without the aid of a distinguishing color • Generate vectors for the MCU